20 papers · 1 filter
One Policy, Many Embodiments: Unified Camera-Centric Action Geometry Pre-training for Heterogeneous Embodied Manipulation
Xiaomi Embodied Intelligence Team, University of Macau, : +21
Scaling generalist vision-language-action (VLA) policies is severely bottlenecked by the inherent heterogeneity of embodied data, which spans diverse robot morphologies, camera con…
What Matters for Latent Actions in Robot Learning
Xizhou Bu, Qingda Hu, Lei Zhou +13
Latent Action Models (LAMs) have emerged as a promising paradigm for enabling robot learning to leverage large-scale unlabeled videos through latent actions that serve as compact s…
Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
Xiaomi Robotics Team, Jun Guo, Piaopiao Jin +31
We present Xiaomi-Robotics-1, a foundational vision-language-action (VLA) model capable of (1) following diverse language instructions to perform a wide range of mobile manipulatio…
FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation
Lingfeng Zhang, Zeying Gong, Xiaoshuai Hao +7
Vision-and-language navigation (VLN) in continuous environments requires an agent to ground instructions in egocentric observations while maintaining spatial understanding across l…
Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning
Ziyang Yao, Haochen Liu, Yuncheng Jiang +10
Autonomous driving requires reasoning about how ego actions shape future world evolution, rather than merely mapping observations to actions. However, most end-to-end methods rely…
OneVLA: A Unified Framework for Embodied Tasks
Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang +10
Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact physically with their surroun…
SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Framework
Tianshu Wu, Xiangqi Kong, Yue Chen +5
Building humanoid robots capable of generalizable whole-body loco-manipulation in the real world remains a fundamental challenge. Existing methods either rely on laborious task-spe…
Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives
Junli Wang, Zhihua Hua, Xueyi Liu +7
Existing imitation learning methods for end-to-end autonomous driving predominantly learn from successful demonstrations by minimizing geometric deviations from expert trajectories…
RotVLA: Rotational Latent Action for Vision-Language-Action Model
Qiwei Li, Xicheng Gong, Xinghang Li +5
Latent Action Models (LAMs) have emerged as an effective paradigm for handling heterogeneous datasets during Vision-Language-Action (VLA) model pretraining, offering a unified acti…
Walk With Me: Long-Horizon Social Navigation for Human-Centric Outdoor Assistance
Lingfeng Zhang, Xiaoshuai Hao, Xizhou Bu +11
Assisting humans in open-world outdoor environments requires robots to translate high-level natural-language intentions into safe, long-horizon, and socially compliant navigation b…
Learning from Mistakes: Post-Training for Driving VLA with Takeover Data
Yinfeng Gao, Deqing Liu, Qichao Zhang +7
Current Vision-Language-Action (VLA) paradigms in end-to-end autonomous driving rely on offline training from static datasets, leaving them vulnerable to distribution shift. Recent…
PerlAD: Towards Enhanced Closed-loop End-to-end Autonomous Driving with Pseudo-simulation-based Reinforcement Learning
Yinfeng Gao, Qichao Zhang, Deqing Liu +8
End-to-end autonomous driving policies based on Imitation Learning (IL) often struggle in closed-loop execution due to the misalignment between inadequate open-loop training object…
Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
Yinan Zheng, Tianyi Tan, Bin Huang +11
Diffusion models have become a popular choice for decision-making tasks in robotics, and more recently, are also being considered for solving autonomous driving tasks. However, the…
The RoboSense Challenge: Sense Anything, Navigate Anywhere, Adapt Across Platforms
Lingdong Kong, Shaoyuan Xie, Zeying Gong +135
Autonomous systems are increasingly deployed in open and dynamic environments -- from city streets to aerial and indoor spaces -- where perception models must remain reliable under…
SocialNav-Map: Dynamic Mapping with Human Trajectory Prediction for Zero-Shot Social Navigation
Lingfeng Zhang, Erjia Xiao, Xiaoshuai Hao +7
Social navigation in densely populated dynamic environments poses a significant challenge for autonomous mobile robots, requiring advanced strategies for safe interaction. Existing…
RoboAfford++: A Generative AI-Enhanced Dataset for Multimodal Affordance Learning in Robotic Manipulation and Navigation
Xiaoshuai Hao, Yingbo Tang, Lingfeng Zhang +6
Robotic manipulation and navigation are fundamental capabilities of embodied intelligence, enabling effective robot interactions with the physical world. Achieving these capabiliti…
Learning to Navigate Socially Through Proactive Risk Perception
Erjia Xiao, Lingfeng Zhang, Yingbo Tang +7
In this report, we describe the technical details of our submission to the IROS 2025 RoboSense Challenge Social Navigation Track. This track focuses on developing RGBD-based percep…
Team Xiaomi EV-AD VLA: Caption-Guided Retrieval System for Cross-Modal Drone Navigation -- Technical Report for IROS 2025 RoboSense Challenge Track 4
Lingfeng Zhang, Erjia Xiao, Yuchen Zhang +7
Cross-modal drone navigation remains a challenging task in robotics, requiring efficient retrieval of relevant images from large-scale databases based on natural language descripti…
MiMo-Embodied: X-Embodied Foundation Model Technical Report
Xiaoshuai Hao, Lei Zhou, Zhijian Huang +41
We open-source MiMo-Embodied, the first cross-embodied foundation model to successfully integrate and achieve state-of-the-art performance in both Autonomous Driving and Embodied A…
DSBench: A Comprehensive Benchmark for Evaluating External and In-Cabin Risks
Xianhui Meng, Yuchen Zhang, Zhijian Huang +12
Vision-Language Models (VLMs) show great promise for autonomous driving, but their suitability for safety-critical scenarios is largely unexplored, raising safety concerns. This is…